Either you missed it or I don't fully understand why you were confused when you asked this question, but the same Bishop argues (in the same sentence where he says what you are wondering about) why tree-based models are popular in fields such as medical diagnosis.
A key property of tree-based models, which makes them popular in fields such as medical diagnosis, for example, is that they are readily interpretable by humans because they correspond to a sequence of binary decisions applied to the individual input variables. For instance, to predict a patient's disease, we might first ask "is their temperature greater than some threshold?". If the answer is yes, then we might next ask “is their blood pressure less than some threshold?". Each leaf of the tree is then associated with a specific diagnosis.
Nowadays, with the successes of neural networks (for example, in Go, Atari, image classification and segmentation, and even machine translation), which are not easily interpretable (so they are known as black-box models), there are always more studies/research on interpretable models or techniques to interpret black-box models, such as neural networks. You can take a look at this answer for a list of explainable/interpretable AI approaches that have been developed. This post contains many answers that further motivate the need for explainble AI.